Gpu based server in a distributed file system

ABSTRACT

A plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of a plurality of storage devices. A plurality of failure resilient stripes is distributed across the plurality of storage devices such that each of the plurality of failure resilient stripes spans a plurality of the storage devices. A graphics processing unit is operable to access data files from the failure resilient stripes, while bypassing a kernel page cache. Furthermore, these data files may be accessed in parallel by the graphics processing unit.

PRIORITY CLAIM

This application claims priority to the following application, which ishereby incorporated herein by reference:

U.S. provisional patent application 62/686,964 titled “GPU BASED SERVERIN A DISTRIBUTED FILE SYSTEM” filed on Jun. 19, 2018.

BACKGROUND

Limitations and disadvantages of conventional approaches to data storagewill become apparent to one of skill in the art, through comparison ofsuch approaches with some aspects of the present method and system setforth in the remainder of this disclosure with reference to thedrawings.

INCORPORATION BY REFERENCE

U.S. patent application Ser. No. 15/243,519 titled “Distributed ErasureCoded Virtual File System” is hereby incorporated herein by reference inits entirety.

BRIEF SUMMARY

Methods and systems are provided for a distributed file systemcomprising a GPU substantially as illustrated by and/or described inconnection with at least one of the figures, as set forth morecompletely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates various example configurations of a distributed filesystem in accordance with aspects of this disclosure.

FIG. 2 illustrates an example configuration of a distributed file systemnode in accordance with aspects of this disclosure.

FIG. 3 illustrates another representation of a distributed file systemin accordance with an example implementation of this disclosure.

FIG. 4 illustrates an example of in a distributed file system comprisinga GPU based server in accordance with an example implementation of thisdisclosure.

FIG. 5 is a flowchart illustrating an example method for using a GPUbased server in a distributed file system.

DETAILED DESCRIPTION

Traditionally, file systems use a centralized control over the metadatastructure (e.g., directories, files, attributes, file contents). If alocal file system is accessible from a single server and that serverfails, the file system's data may be lost if as there is no furtherprotection. To add protection, some file systems (e.g., as provided byNetApp) have used one or more pairs of controllers in an active-passivemanner to replicate the metadata across two or more computers. Othersolutions have used multiple metadata servers in a clustered way (e.g.,as provided by IBM GPFS, Dell EMC Isilon, Lustre, etc.). However,because the number of metadata servers in a traditional clustered systemis limited to small numbers, such systems are unable to scale.

The systems in this disclosure are applicable to small clusters and canalso scale to many, many thousands of nodes. An example embodiment isdiscussed regarding non-volatile memory (NVM), for example, flash memorythat comes in the form of a solid-state drive (SSD). The NVM may bedivided into 4 kB blocks and 128 MB chunks. Extents may be stored involatile memory, e.g., RAM for fast access, backed up by NVM storage aswell. An extent may store pointers for blocks, e.g., 256 pointers to 1MB of data stored in blocks. In other embodiments, larger or smallermemory divisions may also be used. Metadata functionality in thisdisclosure may be effectively spread across many servers. For example,in cases of “hot spots” where a large load is targeted at a specificportion of the file system's namespace, this load can be distributedacross a plurality of nodes.

FIG. 1 illustrates various example configurations of a distributed filesystem in accordance with aspects of this disclosure. Shown in FIG. 1 isa local area network (LAN) 102 comprising one or more nodes 120 (indexedby integers from 1 to J, for j≥1), and optionally comprising (indicatedby dashed lines): one or more dedicated storage nodes 106 (indexed byintegers from 1 to M, for M≥1), one or more compute nodes 104 (indexedby integers from 1 to N, for N≥1), and/or an edge router that connectsthe LAN 102 to a remote network 118. The remote network 118 optionallycomprises one or more storage services 114 (indexed by integers from 1to K, for K≥1), and/or one or more dedicated storage nodes 115 (indexedby integers from 1 to L, for L≥1).

Each node 120 _(j) (j an integer, where 1≤j≤J) is a networked computingdevice (e.g., a server, personal computer, or the like) that comprisescircuitry for running processes (e.g., client processes) either directlyon an operating system of the device 104 n and/or in one or more virtualmachines running in the device 104 _(n).

The compute nodes 104 are networked devices that may run a virtualfrontend without a virtual backend. A compute node 104 may run a virtualfrontend by taking a single root input/output virtualization (SR-IOV)into the network interface card (NIC) and consuming a complete processorcore. Alternatively, the compute node 104 may run the virtual frontendby routing the networking through a Linux kernel networking stack andusing kernel process scheduling, thus not having the requirement of afull core. This is useful if a user does not want to allocate a completecore for the file system or if the networking hardware is incompatiblewith the file system requirements.

FIG. 2 illustrates an example configuration of a node in accordance withaspects of this disclosure. A node comprises a frontend 202 and driver208, a memory controller 204, a backend 206, and an SSD agent 214. Thefrontend 202 may be a virtual frontend; the memory controller 204 may bea virtual memory controller; the backend 206 may be a virtual backend;and the driver 208 may be a virtual drivers. As used in this disclosure,a virtual file system (VFS) process is a process that implements one ormore of: the frontend 202, the memory controller 204, the backend 206,and the SSD agent 214. Thus, in an example implementation, resources(e.g., processing and memory resources) of the node may be shared amongclient processes and VFS processes. The processes of the VFS may beconfigured to demand relatively small amounts of the resources tominimize the impact on the performance of the client applications. Thefrontend 202, the memory controller 204, and/or the backend 206 and/orthe SSD agent 214 may run on a processor of the host 201 or on aprocessor of the network adaptor 218. For a multi-core processor,different VFS process may run on different cores, and may run adifferent subset of the services. From the perspective of the clientprocess(es) 212, the interface with the virtual file system isindependent of the particular physical machine(s) on which the VFSprocess(es) are running. Client processes only require driver 208 andfrontend 202 to be present in order to serve them.

The node may be implemented as a single tenant server (e.g., bare-metal)running directly on an operating system or as a virtual machine (VM)and/or container (e.g., a Linux container (LXC)) within a bare-metalserver. The VFS may run within an LXC container as a VM environment.Thus, inside the VM, the only thing that may run is the LXC containercomprising the VFS. In a classic bare-metal environment, there areuser-space applications and the VFS runs in an LXC container. If theserver is running other containerized applications, the VFS may runinside an LXC container that is outside the management scope of thecontainer deployment environment (e.g. Docker).

The node may be serviced by an operating system and/or a virtual machinemonitor (VMM) (e.g., a hypervisor). The VMM may be used to create andrun the node on a host 201. Multiple cores may reside inside the singleLXC container running the VFS, and the VFS may run on a single host 201using a single Linux kernel. Therefore, a single host 201 may comprisemultiple frontends 202, multiple memory controllers 204, multiplebackends 206, and/or one or more drivers 208. A driver 208 may run inkernel space outside the scope of the LXC container.

A SR-IOV PCIe virtual function may be used to run the networking stack210 in user space 222. SR-IOV allows the isolation of PCI Express, suchthat a single physical PCI Express can be shared on a virtualenvironment and different virtual functions may be offered to differentvirtual components on a single physical server machine. The I/O stack210 enables the VFS node to bypasses the standard TCP/IP stack 220 andcommunicate directly with the network adapter 218. A Portable OperatingSystem Interface for uniX (POSIX) VFS functionality may be providedthrough lockless queues to the VFS driver 208. SR-IOV or full PCIephysical function address may also be used to run non-volatile memoryexpress (NVMe) driver 214 in user space 222, thus bypassing the Linux IOstack completely. NVMe may be used to access non-volatile storage device216 attached via a PCI Express (PCIe) bus. The non-volatile storagedevice 220 may be, for example, flash memory that comes in the form of asolid-state drive (SSD) or Storage Class Memory (SCM) that may come inthe form of an SSD or a memory module (DIMM). Other example may includestorage class memory technologies such as 3D-XPoint.

The SSD may be implemented as a networked device by coupling thephysical SSD 216 with the SSD agent 214 and networking 210.Alternatively, the SSD may be implemented as a network-attached NVMe SSD242 or 244 by using a network protocol such as NVMe-oF (NVMe overFabrics). NVMe-oF may allow access to the NVMe device using redundantnetwork links, thereby providing a higher level or resiliency. Networkadapters 226, 228, 230 and 232 may comprise hardware acceleration forconnection to the NVMe SSD 242 and 244 to transform them into networkedNVMe-oF devices without the use of a server. The NVMe SSDs 242 and 244may each comprise two physical ports, and all the data may be accessedthrough either of these ports.

Each client process/application 212 may run directly on an operatingsystem or may run in a virtual machine and/or container serviced by theoperating system and/or hypervisor. A client process 212 may read datafrom storage and/or write data to storage in the course of performingits primary function. The primary function of a client process 212,however, is not storage-related (i.e., the process is only concernedthat its data is reliably stored and is retrievable when needed, and notconcerned with where, when, or how the data is stored). Exampleapplications which give rise to such processes include: email servers,web servers, office productivity applications, customer relationshipmanagement (CRM), animated video rendering, genomics calculation, chipdesign, software builds, and enterprise resource planning (ERP).

A client application 212 may make a system call to the kernel 224 whichcommunicates with the VFS driver 208. The VFS driver 208 puts acorresponding request on a queue of the VFS frontend 202. If several VFSfrontends exist, the driver may load balance accesses to the differentfrontends, making sure a single file/directory is always accessed viathe same frontend. This may be done by sharding the frontend based onthe ID of the file or directory. The VFS frontend 202 provides aninterface for routing file system requests to an appropriate VFS backendbased on the bucket that is responsible for that operation. Theappropriate VFS backend may be on the same host or it may be on anotherhost.

A VFS backend 206 hosts several buckets, each one of them services thefile system requests that it receives and carries out tasks to otherwisemanage the virtual file system (e.g., load balancing, journaling,maintaining metadata, caching, moving of data between tiers, removingstale data, correcting corrupted data, etc.)

A VFS SSD agent 214 handles interactions with a respective storagedevice 216. This may include, for example, translating addresses, andgenerating the commands that are issued to the storage device (e.g., ona SATA, SAS, PCIe, or other suitable bus). Thus, the VFS SSD agent 214operates as an intermediary between a storage device 216 and the VFSbackend 206 of the virtual file system. The SSD agent 214 could alsocommunicate with a standard network storage device supporting a standardprotocol such as NVMe-oF (NVMe over Fabrics).

FIG. 3 illustrates another representation of a distributed file systemin accordance with an example implementation of this disclosure. In FIG.3, the element 302 represents memory resources (e.g., DRAM and/or othershort-term memory) and processing (e.g., x86 processor(s), ARMprocessor(s), NICs, ASICs, FPGAs, and/or the like) resources of variousnode(s) (compute, storage, and/or VFS) on which resides a virtual filesystem, such as described regarding FIG. 2 above. The element 308represents the one or more physical storage devices 216 which providethe long term storage of the virtual file system.

As shown in FIG. 3, the physical storage is organized into a pluralityof distributed failure resilient address spaces (DFRASs) 318. Each ofwhich comprises a plurality of chunks 310, which in turn comprises aplurality of blocks 312. The organization of blocks 312 into chunks 310is only a convenience in some implementations and may not be done in allimplementations. Each block 312 stores committed data 316 (which maytake on various states, discussed below) and/or metadata 314 thatdescribes or references committed data 316.

The organization of the storage 308 into a plurality of DFRASs enableshigh performance parallel commits from many—perhaps all—of the nodes ofthe virtual file system (e.g., all nodes 104 ₁-104 _(N), 106 ₁-106 _(M),and 120 ₁-120 _(J) of FIG. 1 may perform concurrent commits inparallel). In an example implementation, each of the nodes of thevirtual file system may own a respective one or more of the plurality ofDFRAS and have exclusive read/commit access to the DFRASs that it owns.

Each bucket owns a DFRAS, and thus does not need to coordinate with anyother node when writing to it. Each bucket may build stripes across manydifferent chunks on many different SSDs, thus each bucket with its DFRAScan choose what “chunk stripe” to write to currently based on manyparameters, and there is no coordination required in order to do so oncethe chunks are allocated to that bucket. All buckets can effectivelywrite to all SSDs without any need to coordinate.

Each DFRAS being owned and accessible by only its owner bucket that runson a specific node allows each of the nodes of the VFS to control aportion of the storage 308 without having to coordinate with any othernodes (except during [re]assignment of the buckets holding the DFRASsduring initialization or after a node failure, for example, which may beperformed asynchronously to actual reads/commits to storage 308). Thus,in such an implementation, each node may read/commit to its buckets'DFRASs independently of what the other nodes are doing, with norequirement to reach any consensus when reading and committing tostorage 308. Furthermore, in the event of a failure of a particularnode, the fact the particular node owns a plurality of buckets permitsmore intelligent and efficient redistribution of its workload to othernodes (rather the whole workload having to be assigned to a single node,which may create a “hot spot”). In this regard, in some implementationsthe number of buckets may be large relative to the number of nodes inthe system such that any one bucket may be a relatively small load toplace on another node. This permits fine grained redistribution of theload of a failed node according to the capabilities and capacity of theother nodes (e.g., nodes with more capabilities and capacity may begiven a higher percentage of the failed nodes buckets).

To permit such operation, metadata may be maintained that maps eachbucket to its current owning node such that reads and commits to storage308 can be redirected to the appropriate node.

Load distribution is possible because the entire file system metadataspace (e.g., directory, file attributes, content range in the file,etc.) can be broken (e.g., chopped or sharded) into small, uniformpieces (e.g., “shards”). For example, a large system with 30k serverscould chop the metadata space into 128k or 256k shards.

Each such metadata shard may be maintained in a “bucket.” Each VFS nodemay have responsibility over several buckets. When a bucket is servingmetadata shards on a given backend, the bucket is considered “active” orthe “leader” of that bucket. Typically, there are many more buckets thanVFS nodes. For example, a small system with 6 nodes could have 120buckets, and a larger system with 1,000 nodes could have 8k buckets.

Each bucket may be active on a small set of nodes, typically 5 nodesthat that form a penta-group for that bucket. The cluster configurationkeeps all participating nodes up-to-date regarding the penta-groupassignment for each bucket.

Each penta-group monitors itself. For example, if the cluster has 10kservers, and each server has 6 buckets, each server will only need totalk with 30 different servers to maintain the status of its buckets (6buckets will have 6 penta-groups, so 6*5=30). This is a much smallernumber than if a centralized entity had to monitor all nodes and keep acluster-wide state. The use of penta-groups allows performance to scalewith bigger clusters, as nodes do not perform more work when the clustersize increases. This could pose a disadvantage that in a “dumb” mode asmall cluster could actually generate more communication than there arephysical nodes, but this disadvantage is overcome by sending just asingle heartbeat between two servers with all the buckets they share (asthe cluster grows this will change to just one bucket, but if you have asmall 5 server cluster then it will just include all the buckets in allmessages and each server will just talk with the other 4). Thepenta-groups may decide (i.e., reach consensus) using an algorithm thatresembles the Raft consensus algorithm.

Each bucket may have a group of compute nodes that can run it. Forexample, five VFS nodes can run one bucket. However, only one of thenodes in the group is the controller/leader at any given moment.Further, no two buckets share the same group, for large enough clusters.If there are only 5 or 6 nodes in the cluster, most buckets may sharebackends. In a reasonably large cluster there many distinct node groups.For example, with 26 nodes, there are more than 64,000 (26!/5!*(26−5)!)possible five-node groups (i.e., penta-groups).

All nodes in a group know and agree (i.e., reach consensus) on whichnode is the actual active controller (i.e., leader) of that bucket. Anode accessing the bucket may remember (“cache”) the last node that wasthe leader for that bucket out of the (e.g., five) members of a group.If it accesses the bucket leader, the bucket leader performs therequested operation. If it accesses a node that is not the currentleader, that node indicates the leader to “redirect” the access. Ifthere is a timeout accessing the cached leader node, the contacting nodemay try a different node of the same penta-group. All the nodes in thecluster share common “configuration” of the cluster, which allows thenodes to know which server may run each bucket.

Each bucket may have a load/usage value that indicates how heavily thebucket is being used by applications running on the file system. Forexample, a server node with 11 lightly used buckets may receive anotherbucket of metadata to run before a server with 9 heavily used buckets,even though there will be an imbalance in the number of buckets used.Load value may be determined according to average response latencies,number of concurrently run operations, memory consumed or other metrics.

Redistribution may also occur even when a VFS node does not fail. If thesystem identifies that one node is busier than the others based on thetracked load metrics, the system can move (i.e., “fail over”) one of itsbuckets to another server that is less busy. However, before actuallyrelocating a bucket to a different host, load balancing may be achievedby diverting writes and reads. Since each write may end up on adifferent group of nodes, decided by the DFRAS, a node with a higherload may not be selected to be in a stripe to which data is beingwritten. The system may also opt to not serve reads from a highly loadednode. For example, a “degraded mode read” may be performed, wherein ablock in the highly loaded node is reconstructed from the other blocksof the same stripe. A degraded mode read is a read that is performed viathe rest of the nodes in the same stripe, and the data is reconstructedvia the failure protection. A degraded mode read may be performed whenthe read latency is too high, as the initiator of the read may assumethat that node is down. If the load is high enough to create higher readlatencies, the cluster may revert to reading that data from the othernodes and reconstructing the needed data using the degraded mode read.

Each bucket manages its own distributed erasure coding instance (i.e.,DFRAS 518) and does not need to cooperate with other buckets to performread or write operations. There are potentially thousands of concurrent,distributed erasure coding instances working concurrently, each for thedifferent bucket. This is an integral part of scaling performance, as iteffectively allows any large file system to be divided into independentpieces that do not need to be coordinated, thus providing highperformance regardless of the scale.

Each bucket handles all the file systems operations that fall into itsshard. For example, the directory structure, file attributes and filedata ranges will fall into a particular bucket's jurisdiction.

An operation done from any frontend starts by finding out what bucketowns that operation. Then the backend leader, and the node, for thatbucket is determined. This determination may be performed by trying thelast-known leader. If the last-known leader is not the current leader,that node may know which node is the current leader. If the last-knownleader is not part of the bucket's penta-group anymore, that backendwill let the front end know that it should go back to the configurationto find a member of the bucket's penta-group. The distribution ofoperations allows complex operations to be handled by a plurality ofservers, rather than by a single computer in a standard system.

If the cluster of size is small (e.g., 5) and penta-groups are used,there will be buckets that share the same group. As the cluster sizegrows, buckets are redistributed such that no two groups are identical.

A graphics processing unit (GPU) is a specialized electronic circuitdesigned to rapidly manipulate and alter memory to accelerate thecreation of images in a frame buffer intended for output to a displaydevice. GPUs are used in embedded systems, mobile phones, personalcomputers, workstations, and game consoles. Modern GPUs are veryefficient at manipulating computer graphics and image processing, andtheir highly parallel structure makes them more efficient thangeneral-purpose CPUs for algorithms where the processing of large blocksof data is done in parallel. In a personal computer, a GPU can bepresent on a video card, or it can be embedded on the motherboard or—incertain CPUs—on the CPU die.

The term GPU was popularized by NVIDIA. NVIDIA's GPU was presented as a“single-chip processor with integrated transform, lighting, trianglesetup/clipping, and rendering engines.”

FIG. 4 illustrates an example of in a distributed file system comprisinga GPU based server 409 in accordance with an example implementation ofthis disclosure. In addition to the GPU based server 409, thedistributed file system comprises a computing device (e.g., a CPU basedserver 401) and non-volatile system memory 419 (e.g., a plurality ofSSDs 427 a, 427 b and 427 c).

The CPU based server 401 comprises a frontend 403 and a backend 405. Thebackend 405 comprises at least one bucket 407. The plurality of SSDs 427a, 427 b and 427 c may be configured into a plurality of blocks, e.g.,block a, block b and block c.

Each bucket in a backend is operable to build one or more failureresilient stripes 429 comprising a plurality of blocks. For example,with 10 blocks, 8 blocks of data could be protected with 2 blocks oferror protection/correction (i.e., using an 8+2 stripe). Likewise, with10 failure domains, 6 blocks of data could be protected with 4 blocks oferror protection/correction (i.e., using a 6+4 stripe).

For illustration, 3 storage devices and 1 stripe are illustrated in FIG.4. A different number of storage devices and stripes may be used withoutdeviating from this disclosure. Bucket 407 is operable to build failureresilient stripe 429, which comprises block a, block b and block c. Eachstorage block of the plurality of storage blocks in a particular failureresilient stripe may be located in a different storage device of theplurality of storage devices.

The GPU based server 409 may be connected to the CPU based server 401over a PCIe interface 417 and may access the system memory 419 via asystem memory bus 423. When the GPU 409 performs computations, however,the GPU 409 uses dedicated GPU memory 421 via a graphics memory bus 425.The dedicated memory for NVIDIA's GPU, for example, is RAM connecteddirectly to the GPU with a high bandwidth memory interface. Thus, datastored on the GPU memory 421 may be transferred from the system memory419 through the PCIe interface 417.

Data being transferred from the SSDs 427 a, 427 b and 427 c may first beplaced in the kernel page cache 431. Then, the data may be copied fromthe kernel page cache 431 to the application user space memory 433 inthe system main memory 419. And finally, the data may be copied from theapplication user space memory 433 into the GPU memory 421. Since manyGPU applications do not need to access system main memory, there are twowasted copies. Further, these GPU applications are very performancesensitive, so getting data directly to the GPU memory is important.

The Portable Operating System Interface (POSIX) is a family of standardsspecified by the IEEE Computer Society for maintaining compatibilitybetween operating systems. When standard POSIX applications are written,they assume that files are being kept open for a long time. The standardPOSIX process comprises finding the inode (a series of lookup requests),opening the file, accessing the file, and finally closing. If a file isopen for a long time, the standard POSIX process is useful.

GPU based applications, however, deal with a large number of small files(e.g., images, voice samples, text snippets, etc.) in the GPU memory.The POSIX overhead in GPU based applications is quite high.

In accordance with one or more embodiments of the present disclosure,when an application uses GPU data, the data may be read directly from adistributed file system that comprises both system memory 419 andgraphics memory 421. For example, the graphics processing device 409 maybe operable to transfer storage blocks a, b or c to the volatile memorydevice 421 while bypassing the kernel page cache 431. GPU basedapplications may be provided to place the content of the small files inuser-space memory 433 of the applications, thereby bypassing the POSIXprotocol and the kernel page cache 431 and adjusting efficient key-valueaccesses. Furthermore, the graphics processing device 409 may beoperable to access a plurality of storage blocks in parallel via a highbandwidth interface from the SSDs 427 a, 427 b and 427 c.

The GPU based application may provide a string of a complete file name(e.g., “/dir1/dir2/ . . . /dirN/filename”) and a memory location. Thesystem will communicate directly with the user space front end andbypass the kernel driver altogether. The system may fetch the contentsof the GPU based application files in parallel and place them in the GPUmemory 421, thereby eliminating the POSIX overhead. Also, by eliminatingthe need to copy to and from the kernel page cache 431, latency may bereduced. The GPU application may copy the data over the PCIe interface417 to GPU memory 421 and notify the GPU 409 that processing may begin(e.g., by ringing a doorbell). For example, the computing devicefrontend 403 may indicate to a GPU and/or the GPU based server 409 thatdata required for a graphics operation is available in the volatilememory device 421.

Once the frontend 403 verifies that data is fully placed in GPU memory421, the frontend 403 may notify the GPU application that the data isthere, so then the user application can launch the GPU procedure to workon that data. However, because waking up a GPU client application alsotakes time and increases latency, the application may store the GPUprocedure in the GPU, and the GPU may wait for a doorbell (i.e., anindication to the hardware) that all the data is placed in the rightlocation. The frontend 403 can then ring the doorbell of the GPU tostart processing the data.

Data may also be written directly into GPU memory 421. Instead ofinstructing the CPU based sever 401 (e.g., on a network interface card)to write the incoming packets to system memory 419, data packets may bewritten to GPU memory 421 using GPU-Direct and RDMA techniques. Withdirect GPU memory access, a key-value access will place data directly inGPU memory 421.

The GPU based server 409 may comprise a GPU frontend 411 and a GPUbackend 413. The GPU backend 413 may comprise at least one GPU bucket415. The bucket 415 on the GPU backend 413 may be operable to managedata in the volatile memory device 421. The computing device 401 and thegraphics processing device 409 may be coupled via a network interface435. The GPU frontend 411 may determine a location of data required fora graphics operation. This data may be received directly over thenetwork interface 435 from a computing device. For example, the GPUfrontend 411 may be in communication with the frontend 403, which maydetermine that data required for a graphics operation is located in afailure-protected stripe 429 that is led by a bucket 407 in a backend405 of the computing device 401. The GPU frontend 411 may indicate to agraphics processing unit (GPU) when data required for a graphicsoperation is available in the volatile memory device 421.

FIG. 5 is a flowchart illustrating an example method for using a GPU ina distributed file system. In block 501, a plurality offailure-protected stripes is built using a computing device, such thateach of the plurality of failure-protected stripes comprises a pluralityof storage blocks and each storage block of the plurality of storageblocks is located in a different flash memory device of a plurality offlash memory devices.

In block 503, the location of data for a graphics operation isdetermined. If data for a graphics operation is located in a failureprotected-stripe, the graphics data in one or more blocks of a failureresilient stripe is transferred to a volatile memory device in block507. This data may be transferred in parallel over a high bandwidth busthat bypasses the kernel page cache. In block 509, the graphicsoperation may be performed on the transferred data using a graphicsprocessing device.

If data for the graphics operation is not located in a failureprotected-stripe, the graphics data may transferred directly to avolatile memory device from the network via a network interface in block505. Once the data required for the graphics operation is available inthe volatile memory device, the availability is indicated to the GPU.

While the present method and/or system has been described with referenceto certain implementations, it will be understood by those skilled inthe art that various changes may be made and equivalents may besubstituted without departing from the scope of the present methodand/or system. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the presentdisclosure without departing from its scope. Therefore, it is intendedthat the present method and/or system not be limited to the particularimplementations disclosed, but that the present method and/or systemwill include all implementations falling within the scope of theappended claims.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (i.e. hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprisefirst “circuitry” when executing a first one or more lines of code andmay comprise second “circuitry” when executing a second one or morelines of code. As utilized herein, “and/or” means any one or more of theitems in the list joined by “and/or”. As an example, “x and/or y” meansany element of the three-element set {(x), (y), (x, y)}. In other words,“x and/or y” means “one or both of x and y”. As another example, “x, y,and/or z” means any element of the seven-element set {(x), (y), (z), (x,y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means“one or more of x, y and z”. As utilized herein, the term “exemplary”means serving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “e.g.,” and “for example” set off lists ofone or more non-limiting examples, instances, or illustrations. Asutilized herein, circuitry is “operable” to perform a function wheneverthe circuitry comprises the necessary hardware and code (if any isnecessary) to perform the function, regardless of whether performance ofthe function is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, etc.).

What is claimed is: 1-20. (canceled)
 21. A system, the systemcomprising: a central processing unit (CPU); a CPU memory running anoperating system (OS) of the CPU; an accelerator operably coupled to theCPU via a bus; an accelerator memory operably coupled to theaccelerator; and a plurality of storage nodes operably coupled to theCPU, wherein: the plurality of storage nodes is logically segmented intoa plurality of stripes, and the CPU is configured to write data, fromany of the plurality of stripes, into the accelerator memory, whilebypassing the OS and the CPU memory.
 22. The system of claim 21, whereinthe accelerator comprises a graphics processing unit (GPU).
 23. Thesystem of claim 21, wherein the CPU memory comprises a random accessmemory (RAM).
 24. The system of claim 21, wherein the accelerator islocated on a network interface card (NIC).
 25. The system of claim 21,wherein the CPU is located on a NIC.
 26. The system of claim 21, whereinthe accelerator and the CPU are operably coupled via a PCI Express(PCIe) bus
 27. The system of claim 21, wherein a distributed file systemcomprises the plurality of storage nodes and the CPU.
 28. The system ofclaim 21, wherein writing from a stripe of the plurality of stripes intothe accelerator memory bypasses a kernel page cache.
 29. The system ofclaim 21, wherein: the accelerator is operable to determine that datarequired for an operation is located in a stripe of the plurality ofstripes; and a frontend interface is operable to notify the acceleratorwhen the data is available in the local memory.
 30. The system of claim21, wherein writing data into the accelerator memory comprises usingRDMA.
 31. A method, the method comprising: generating a plurality ofstripes, via one or more compute nodes, by segmenting a plurality ofstorage nodes; and writing data, via a bus from any of the plurality ofstripes, into an accelerator memory used by an accelerator, whereinwriting the data comprises bypassing an operating system mechanism usedfor an access of the plurality of stripes.
 32. The method of claim 31,wherein the accelerator comprises a graphics processing unit (GPU). 33.The method of claim 31, wherein the accelerator memory comprises arandom access memory (RAM).
 34. The method of claim 31, wherein theaccelerator and the accelerator memory are located on a networkinterface card (NIC).
 35. The method of claim 31, wherein a compute nodeof the one or more compute nodes is located on a NIC.
 36. The method ofclaim 31, wherein the accelerator and a compute node of the one or morecompute nodes are operably coupled via a PCI Express (PCIe) bus.
 37. Themethod of claim 31, wherein a distributed file system comprises theplurality of storage nodes and at least one of the one or more computenodes.
 38. The method of claim 31, wherein the method comprises:configuring a compute node, of the one or more compute nodes, to writeinto the accelerator memory via a key-value access; and writing data,from a stripe of the plurality of stripes, into the accelerator memorybypasses a kernel page cache.
 39. The method of claim 31, wherein themethod comprises notifying the accelerator when the data is available inthe local memory.
 40. The method of claim 31, wherein the methodcomprises determining, via a frontend interface, when data required foran operation is located in a stripe of the plurality of stripes.